Genetic Algorithm Scheduler: generating adaptive tables
A timetable can be interpreted as an arbitrary sequence of events. To every event a certain number of time intervals are assigned, each having a starting and an ending.
Table of Contents
Genetic Algorithm (GA) is a type of EA and is regarded as being the most widely known EA in recent times. Scheduling is widely used in schools, colleges and other fields of teaching and working like crash courses, training programs, and other situations of the world where delegation is necessary.
Even though most college administrative work has been computerized, the lecture timetable scheduling is still mostly done manually due to its inherent large and high constraints.
In certain cases, perfectly designed time table is reused for whole generation without any changes, proving to be dull in such situations due to the continuous change of humans. Above all, the problem differs greatly from school to school and educational levels.
While scheduling, even the smallest delegations can take a lot of time and this scenario is even worse when the number of delegations or the number of stakeholders for delegation increases. Other situations include where stakeholders post are reassigned to different departments or courses, resulting in rescheduling of time table causing the need to fill on empty seats urgently.
Other cases that can cause problems is when the number of partakers increase and authorities need to schedule their course to meet the need of current duration and facilities that are available to them.
Key Consideration of GA Scheduler
• Room
• Lecturer
• Students Group
• Class
• Campuses
• Buildings and
• Faculties,
• Departments and
• Programs
Benefits of GA Scheduler
a) A classroom is not assigned to more than one lecture at the same time.
b) An instructor cannot teach more than one class at the same time.
c) Courses for the same year-session students of a department cannot take place at the same time.
d) The classroom for a course should have enough capacity to take students registered in the course.
e) The classroom should be well equipped with required facilities for the classes
f) The lectures are not assigned to time slots, which are in the Instructor’s forbidden time zones.
g) Instructors‟ daily lecture hours should be restricted to be within the allowed maximum hours.
h) As far as possible, classes are scheduled in the instructors preferred time zones.
i) A lunch/dinner break must be scheduled.
j) If possible, the lecture hours for a course should be scheduled consecutively.
k) As far as possible, classes should be scheduled in their corresponding department’s exclusive-use classrooms.
l) The classrooms should be allocated in a manner to minimize the distances between adjacent classes‟ classrooms. E.g. To prevent a student moving from one campus to another campus the same day for different lectures.
m) Unlike the manual timetabling system, the system offers flexibility.
n) It can generate timetables for all departments in the university
o) It allows administration to easily specify constraints required, which is taken into account when generating the timetables
p) It greatly reduces the time needed to generate near-optimal timetables.
q) It provides an easy means for data entry and revision through an intuitive interface.
r) It enables one to generate reports based on the usage of rooms and buildings
s) It greatly simplifies the timetabling process.
t) Create multiple timetables at a time and manage different timetable for multiple departments with customizable notifications and alerts.
u) Provides school with real-time view of courses, classrooms, and lecturers in the timetable.
v) Seamlessly integrated timetable with any calendar application.
w) Automatically send email notifications, reminders and SMS alerts when you create or modify timetables.
x) Allow students and lecturers to view and download their timetables
Requirement for Use
• Hardware Requirement :
– Central server,
– Network infrastructure
• Software Requirement
– High storage capacity
– constant connection from server to clients machines
– High computational Time lapses
• Training Users
– Support Management Policy
– Active training of Users to ease out anticipated computational errors.
Distance Barrier of Institutional Component
– We considered support for clustered systems to aid cross boundary constraint.
• Organization Capacity
– The system is Robust to sustain any volume of data needed to compute depended of the total capacity of users
Conclusion
From these trends objectives for the research were developed and a proposed information system to support all aspects of university timetabling was developed. Its uniqueness, in being derived and built on a foundation of supporting timetable generation, was highlighted and has the capability to generate near optimal timetables for all departments and students groups.